What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Genetics, Gene, Data mining, Candidate gene and Computational biology.
The Transcription factor, Transcriptional regulation, Chromosomal region and Regulator gene research he does as part of his general Gene study is frequently linked to other disciplines of science, such as Arabidopsis, therefore creating a link between diverse domains of science.
His research in the fields of Enhancer overlaps with other disciplines such as Consensus sequence.
The study incorporates disciplines such as Kernel, Microarray analysis techniques, Cluster analysis, Statistical model and Gene chip analysis in addition to Data mining.
His research integrates issues of Proteome, Phenotype, Proteomics, The Internet and Alzheimer's disease in his study of Candidate gene.
His Computational biology study integrates concerns from other disciplines, such as Ontology, Login, Annotation, Regulation of gene expression and TBX1.
His most cited work include:
- PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences (2321 citations)
- BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis (1076 citations)
- DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (1015 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Genetics, Computational biology, Artificial intelligence, Data mining and Machine learning.
His Gene, Genome, Comparative genomic hybridization, Human genome and DNA microarray investigations are all subjects of Genetics research.
In general Gene study, his work on Gene expression and Gene expression profiling often relates to the realm of Arabidopsis, thereby connecting several areas of interest.
His Computational biology research is multidisciplinary, incorporating elements of Bioinformatics and Candidate gene.
His Artificial intelligence research is multidisciplinary, relying on both Text mining and Pattern recognition.
His work carried out in the field of Data mining brings together such families of science as Microarray analysis techniques and Cluster analysis.
He most often published in these fields:
- Genetics (22.84%)
- Computational biology (20.98%)
- Artificial intelligence (17.72%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (17.72%)
- Computational biology (20.98%)
- Machine learning (11.66%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computational biology, Machine learning, Algorithm and Drug repositioning.
His biological study spans a wide range of topics, including Scalability and Pattern recognition.
Yves Moreau focuses mostly in the field of Computational biology, narrowing it down to matters related to Identification and, in some cases, Genetic model and Clinical knowledge.
Yves Moreau has researched Machine learning in several fields, including Matrix decomposition, Task and Gene.
His research investigates the connection between Drug repositioning and topics such as Drug target that intersect with problems in Multi-task learning, Targeted drug delivery, Genomics, Drug development and Risk analysis.
Many of his studies on Disease involve topics that are commonly interrelated, such as Genetics.
Between 2016 and 2021, his most popular works were:
- From hype to reality: data science enabling personalized medicine (97 citations)
- Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery (85 citations)
- GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. (67 citations)
In his most recent research, the most cited papers focused on:
- Gene
- DNA
- Artificial intelligence
Yves Moreau mainly investigates Computational biology, Gene, Artificial intelligence, Machine learning and Drug discovery.
His research in Computational biology intersects with topics in High-throughput screening, Disease, Identification and High content imaging.
Genetics covers Yves Moreau research in Gene.
His work on Chromosomal fragile site, Allele and Gene dosage as part of his general Genetics study is frequently connected to Pan cancer and Genome instability, thereby bridging the divide between different branches of science.
His Artificial intelligence research includes elements of Simple, Theoretical computer science, Protein methods and Structural bioinformatics.
His Machine learning study combines topics from a wide range of disciplines, such as Task and Matrix completion.
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